gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

text stringlengths 0 820
token represents the masked patches that were not encoded.
Another positional encoding vector is added to all patches
and a sequence of transformer blocks decodes these patches
to form the original input image, which is used as the learning
target.
Input Scale-MAE performs a super resolution reconstruc-
tion, where the input image Iis downsampled from a higher
resolution image Ihrat the ground truth GSD. Instead of
targeting the input image, Scale-MAE targets high frequency
and low frequency components of Ihr, which is common in
Laplacian pyramid super resolution models [64], where the
high frequency component is at the same resolution as the
ground truth image Ihrand the low frequency component
is at the same resolution as the input image I, as shown in
Figure 2. Following many works in super resolution [64], the
low frequency target image is obtained by interpolating Ihr
to a much lower resolution, rlowand then interpolating to the
same resolution as the input image I. The high frequency tar-
get image is obtained by downsampling Ihrto another lower
resolution rhigh-low , and then upsampling to the same resolu-
tion as the ground truth image Ihrand subtracting this image
Ihf=Ihr−Ihigh-low . The supplementary material provide
more information on the upsampling/downsampling method-
ology. The key components for Scale-MAE are described
next.
GSD Positional Encoding Images from scale-dependent
domains have a metric which defines the absolute scale for
the image. This metric has different names across domains
and is referred to as the Ground Sample Distance (GSD) in
remote sensing. The GSD is critical to understanding, con-
ceptually, the kinds of features that will be available in an
image. An image with finer GSD (lower number) will have
higher frequency details than an image with coarser GSD
(high number). Models are generally unaware of absolute
scale when learning over a set of data. Specifically, even if
they implicitly learn that all images in a dataset share a vary-
ing resolution from input-space augmentations, then these
models do not explicitly condition on the GSDs encountered
in unseen data.
We extend the positional encoding from Equation (2) to
include GSD by scaling the positional encoding relative to
the land area covered in an image as depicted in Figure 3
and mathematically:
vgsd,x(pos,2i) = sing
Gpos
100002i
D(3)
vgsd,y(pos,2i+ 1) = cosg
Gpos
100002i
D(4)
Figure 3. Ground Sample Distance Positional Encoding (GS-
DPE). (Left) Input images at the same pixel resolution but different
GSDs are shown. The image on the bottom is a subset of the image
on the top. (Center) This overlap in location, albeit at a different
resolution, is reflected in the GSDPE. The finer image with smaller
spatial extent is represented by a corresponding subsection of the
overall sine wave on the bottom. (Right) A standard positional
encoding is strictly dependent on the image resolution and uses the
same embedding for both. The colors behind the sine waves show
the intensity and quantization of the encoding.
where gis the GSD of the image and Gis a reference GSD,
nominally set to 1m. Intuitively, an object imaged at a finer
resolution has more pixels representing it. When imaging the
same object at a coarser resolution, those pixels must map to
fewer pixels. In Equation (4), we interpolate the positional
encoding by a factor ofG
gto account for the ordering of the
coarser set of pixels. This simple idea underpins the GSD
Positional Encoding, visualized in Figure 3.
Scale-MAE decoder The standard MAE learns represen-
tations by tasking a network with reconstructing an image
after masking out most of its pixels. While the standard
MAE decoder reconstructs the input image at the same scale
as its input, the objective of Scale-MAE is to learn multi-
scale representations. We draw on works from progressive
super-resolution such as [56], that learn a high resolution,
high frequency image and a lower resolution low frequency
image, that when combined together, yield the input image
at a higher resolution.
The Scale-MAE introduces a novel decoder which de-
codes to multiple scales with a progressive Laplacian de-
coder architecture, replacing the traditional MAE “decoder”,
which is really a Transfomer encoder. This architecture
consists of three stages: decoding, upsampling, and recon-
struction, which are shown in Figure 2 and detailed below.
Decoding follows the standard MAE decoder where fol-
lowing the encoder, the removed mpatches are then placed
back into their original location in the sequence of patches
where a learned mask token represents the masked patches
that were not encoded, a positional encoding is added, and
then a series of transformer layers decode all patches. In
contrast to the standard MAE decoder, the Scale-MAE de-
coder uses fewer transformer layers (e.g. 3 layers instead of
8), which reduces the parameter complexity as quantified
in Section 5. The output of these layers is then fed into the
upsampling stage.
Upsampling The latent feature maps from the decoding
stage are progressively upsampled to 2x and 4x resolution
using deconvolution blocks, where the first deconvolution

token represents the masked patches that were not encoded.

Another positional encoding vector is added to all patches

and a sequence of transformer blocks decodes these patches

to form the original input image, which is used as the learning

target.

Input Scale-MAE performs a super resolution reconstruc-

tion, where the input image Iis downsampled from a higher

resolution image Ihrat the ground truth GSD. Instead of

targeting the input image, Scale-MAE targets high frequency

and low frequency components of Ihr, which is common in

Laplacian pyramid super resolution models [64], where the

high frequency component is at the same resolution as the

ground truth image Ihrand the low frequency component

is at the same resolution as the input image I, as shown in

Figure 2. Following many works in super resolution [64], the

low frequency target image is obtained by interpolating Ihr

to a much lower resolution, rlowand then interpolating to the

same resolution as the input image I. The high frequency tar-

get image is obtained by downsampling Ihrto another lower

resolution rhigh-low , and then upsampling to the same resolu-

tion as the ground truth image Ihrand subtracting this image

Ihf=Ihr−Ihigh-low . The supplementary material provide

more information on the upsampling/downsampling method-

ology. The key components for Scale-MAE are described

next.

GSD Positional Encoding Images from scale-dependent

domains have a metric which defines the absolute scale for

the image. This metric has different names across domains

and is referred to as the Ground Sample Distance (GSD) in

remote sensing. The GSD is critical to understanding, con-

ceptually, the kinds of features that will be available in an

image. An image with finer GSD (lower number) will have

higher frequency details than an image with coarser GSD

(high number). Models are generally unaware of absolute

scale when learning over a set of data. Specifically, even if

they implicitly learn that all images in a dataset share a vary-

ing resolution from input-space augmentations, then these

models do not explicitly condition on the GSDs encountered

in unseen data.

We extend the positional encoding from Equation (2) to

include GSD by scaling the positional encoding relative to

the land area covered in an image as depicted in Figure 3

and mathematically:

vgsd,x(pos,2i) = sing

Gpos

100002i

D(3)

vgsd,y(pos,2i+ 1) = cosg

Gpos

100002i

D(4)

Figure 3. Ground Sample Distance Positional Encoding (GS-

DPE). (Left) Input images at the same pixel resolution but different

GSDs are shown. The image on the bottom is a subset of the image

on the top. (Center) This overlap in location, albeit at a different

resolution, is reflected in the GSDPE. The finer image with smaller

spatial extent is represented by a corresponding subsection of the

overall sine wave on the bottom. (Right) A standard positional

encoding is strictly dependent on the image resolution and uses the

same embedding for both. The colors behind the sine waves show

the intensity and quantization of the encoding.

where gis the GSD of the image and Gis a reference GSD,

nominally set to 1m. Intuitively, an object imaged at a finer

resolution has more pixels representing it. When imaging the

same object at a coarser resolution, those pixels must map to

fewer pixels. In Equation (4), we interpolate the positional

encoding by a factor ofG

gto account for the ordering of the

coarser set of pixels. This simple idea underpins the GSD

Positional Encoding, visualized in Figure 3.

Scale-MAE decoder The standard MAE learns represen-

tations by tasking a network with reconstructing an image

after masking out most of its pixels. While the standard

MAE decoder reconstructs the input image at the same scale

as its input, the objective of Scale-MAE is to learn multi-

scale representations. We draw on works from progressive

super-resolution such as [56], that learn a high resolution,

high frequency image and a lower resolution low frequency

image, that when combined together, yield the input image

at a higher resolution.

The Scale-MAE introduces a novel decoder which de-

codes to multiple scales with a progressive Laplacian de-

coder architecture, replacing the traditional MAE “decoder”,

which is really a Transfomer encoder. This architecture

consists of three stages: decoding, upsampling, and recon-

struction, which are shown in Figure 2 and detailed below.

Decoding follows the standard MAE decoder where fol-

lowing the encoder, the removed mpatches are then placed

back into their original location in the sequence of patches

where a learned mask token represents the masked patches

that were not encoded, a positional encoding is added, and

then a series of transformer layers decode all patches. In

contrast to the standard MAE decoder, the Scale-MAE de-

coder uses fewer transformer layers (e.g. 3 layers instead of

8), which reduces the parameter complexity as quantified

in Section 5. The output of these layers is then fed into the

upsampling stage.

Upsampling The latent feature maps from the decoding

stage are progressively upsampled to 2x and 4x resolution

using deconvolution blocks, where the first deconvolution